Automatic rating optimization

ABSTRACT

Automatic rating optimization is described. In an embodiment, ratings of a program can be received from one or more rating sources. Based on these ratings, a representation of a content selection mechanism can be sent to potential consumers of the content. Access events for the content can be counted over a duration of time so a determination can be made regarding how the ratings provided by each of the rating sources affect popularity of the content. A weight accorded to ratings received from each of the rating sources can be adjusted based on the determination. Profiles can be established for consumers and/or rating sources.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. application Ser. No.11/611,700, filed Dec. 15, 2006, the entirety of which is incorporatedherein by reference.

BACKGROUND

During the past several years, television viewing habits have beenrapidly changing in response to an increased availability of viewingcontent and to technological advances in the distribution and deliveryof viewing content. As part of the increased availability of viewingcontent, more rare (or niche) programs are becoming available forviewers who may be interested in viewing such programs. Unfortunately,as increasingly larger amounts of content are made available, it isbecoming increasingly difficult for a viewer to locate the rare contentwhich he/she may be interested in viewing. Viewers who are interested inniche content often find themselves searching for the proverbial “needlein a haystack”.

Current program rating and/or recommendation systems typically rateprograms by assigning a number of “stars”, and/or other symbols to theprogram as an indication of the quality of the program. In other cases,programs and movies may be rated by the Motion Picture Association ofAmerica (MPAA) according to MPAA guidelines, and/or may be rated bybroadcast and cable television networks according to the networks'guidelines.

Although current rating and/or recommendation systems can be of someassistance to viewers in locating viewing content, many viewers arebecoming increasingly disenchanted with the current system, and do nottrust the ratings and/or recommendations provided by such systems. Thelimitations of current ratings and/or recommendation systems areparticularly apparent in the case of rare or niche programs. In manycases no ratings have been assigned to such programs, and in cases wherea rating has been assigned, those with an interest in rare or nicheprograms often find that the ratings provided by the current programrating and/or recommendation systems cannot be trusted, and/or aresuited for typical viewer preferences rather than for the uniquepreferences of the niche viewer.

SUMMARY

This summary is provided to introduce basic concepts of automatic ratingoptimization which is further described below in the DetailedDescription. This summary is not intended to identify necessary featuresof the claimed subject matter, nor is it intended for use in determiningthe scope of the claimed subject matter.

In an embodiment of automatic rating optimization, ratings of a programcan be received from one or more rating sources, and these ratings canbe sent to potential viewers of the program. Access events for theprogram can be counted over a duration of time so a determination can bemade regarding how the ratings provided by each of the rating sourcesaffect the popularity of the program. A weight accorded to ratingsreceived from each of the rating sources can be adjusted based on thedetermination.

In another embodiment of automatic rating optimization, a viewer can beassociated with a preference profile based on viewing habits of theview, and rating sources can be associated with the preference profilesbased on viewing habits of the rating sources, so that each ratingsource is associated with one of the viewer and the preference profilesof each of the rating sources can be determined, and a weight accordedto rating received from each of the rating sources can be adjusted onthe determination of the degree of relatedness.

BRIEF DESCRIPTION OF THE DRAWINGS

The same numbers are used throughout the drawings to reference likefeatures and components:

FIG. 1 is a block diagram of an exemplary environment in whichembodiments of automatic rating optimization can be implemented.

FIG. 2 is a block diagram of an exemplary environment in which furtheraspects of embodiments of automatic rating optimization are described.

FIG. 3 is a diagram illustrating exemplary graphs in which furtheraspects of embodiments of automatic rating optimization are described.

FIG. 4 is a flow diagram illustrating exemplary method(s) for automaticrating optimization.

FIG. 5 is a flow diagram illustrating exemplary methods(s) for automaticrating optimization.

FIG. 6 is a block diagram illustrating various components of anexemplary client device in which embodiments of automatic ratingoptimization can be implemented.

FIG. 7 is a diagram illustrating various devices and components in anexemplary entertainment and information system in which embodiments ofautomatic rating optimization can be implemented.

FIG. 8 is a diagram illustrating operating environments that extend thepervious techniques for automatic rating optimization to portablewireless devices.

DETAILED DESCRIPTION

Automatic rating optimization is described in which embodiments providethat input ratings of content are received from one or more individualrating sources, that output ratings are calculated from these inputratings, and that the calculated output ratings are then sent topotential consumers of the content. Access events for the content arecounted over a duration of time and a determination is made regardinghow the ratings provided by each of the rating sources affectedpopularity of the content. A weight accorded to ratings received fromeach of the rating sources is then adjusted based on the determinationso that different rating sources will have a different amount ofinfluence on an overall rating that is assigned to the content.

In the context of this description, the term “ratings” may refer to anymetadata about the content that would be useful in helping potentialconsumers choose content. For example, the content could include one ormore programs. This metadata may include information relating to aduration of the content, details of a plot, technology used to view thecontent, tie-ins, general descriptive data, and the like.

Ratings may refer to input ratings that are provided by individualcontributors, or to output ratings that are derived, aggregated, orcalculated based on these contributions. A prospective viewer of a giveninstance of content may be provided with the output ratings as derivedfrom a variety of contributors or sources.

In some instances, the prospective viewer may be provided with an outputrating from a single source. For example, if reviews from a givenreviewer are accorded sufficient authority or weight, then reviews fromonly that particular reviewer may be provided. As another example,reviews from a single reviewer from the prospective viewer's peer groupmay be provided.

In the context of this description, the term “content” may refer to anydata or subject matter that may be accessed, viewed, listened to,played, utilized, or otherwise consumed. Examples of content mayinclude, but are not limited to, programs, clips, podcasts, games, MP3files, other forms of digital media, or the like. Similarly, the term“consumer” may refer to any subscriber, customer, person or user who mayaccess the content, whether by viewing, listening, playing, or the like.

Automatic rating optimization therefore provides new ways of generatingrating and/or recommendations which take into consideration the affectthat a rating source's prior ratings and/or recommendations have had onthe popularity of the programs. Implementations of automatic ratingoptimization look at results for ratings and/or recommendations providedby various rating sources, correlate the results with the ratingsources, and then adjust various rating sources, correlate the resultswith the rating sources, and then adjust a weight given to differentrating sources so that different rating sources will have a differentimpact on the overall rating for a program. For example, if a ratingsource's prior ratings have had a positive effect on popularity ofprograms which it rated, a higher weight can be accorded ratingsreceived from that rating source. On the other hand, if a ratingsource's prior ratings have had a negative effect on popularity ofprograms which it rated, a lower weight can be accorded ratings receivedfrom that rating source. By adjusting the weight accorded to the ratingsreceived from the different source, a more useful rating can be assignedto a program.

Implementations of automatic rating optimization also provide that theweight accorded to ratings received from the rating sources can beadjusted differently for ratings related to different genres ofprograms. For example, if a particular rating source is very good atrating mysteries, and very poor at rating cooking programs, the weightaccorded to ratings received from the rating source can be adjusteddifferently for programs within these different genres. Implementationsof automatic rating optimization also provide that a rating source canbe excluded from the overall rating. Other implementations of automaticrating optimization provide that a higher or lower weight can beaccorded to all ratings received from a particular rating source.

Embodiments of automatic rating optimization also provide that a viewercan be associated with a preference profile based on viewing habits ofthe viewer, and that rating sources can be associated with preferenceprofiles based on viewing habits of the rating sources so that eachrating source is associated with one of the preference profiles. Adegree of relatedness between the preference profile of the view and thepreference profiles of each of the rating sources is determined, and aweight accorded to ratings received from each of the rating sources isadjusted based on the determining of the degree of relatedness.

Automatic rating optimization therefore provides that preferenceprofiles can be established for viewers and/or rating sources, and thatsuch preference profiles can be used to generate more accurate rating ofprograms. For example, in some implementations, the viewer is presentedwith a rating that has been tailored and/or adjusted for a particulargroup profile. In other implementations, the view is presented with acustomized rating profile based on their personal profile.

In other aspects, ratings given by a viewer may be correlated withratings provided by one or more other reviewers for previouslyexperienced content. In this matter, these correlated rating enable orallow viewers to formulate expectations for new content that has beenrated by these same reviewers. For example, the preferences or opinionsof a given viewer may closely track those of one or more reviewers, suchthat if the reviewer likes or dislikes a given item, the viewer mayexpect to similarly like or dislike that given item.

The program rating presented to one potential viewer can be differentthan the program rating presented to another potential viewer for thesame program, because the potential viewers' profiles can be taken intoconsideration, and the rating can be adjusted accordingly. For example,less weight can be accorded to the ratings received from rating sourcesthat are in profiles distant from the viewer's profile, while moreweight can be accorded to the ratings received from the rating sourcesthat are in profiles proximate the viewer's profile.

While aspects of the described systems and methods for automatic ratingoptimization can be implemented in any number of different computingsystems, environments, television-based entertainment systems, and/orconfigurations, embodiments of automatic rating optimization aredescribed in the context of the following exemplary systemarchitecture(s).

FIG. 1 illustrates an exemplary environment 100 in which embodiments ofautomatic rating optimization can be implemented. The environment 100includes a program rating system 102, viewer(s)/rating source(s) 104,and rating source(s) 106 (the viewer(s) and the viewer(s)/ratingsource(s) are each shown here a person sitting in a chair). As usedherein, the term “program” refers to any television program, movie,on-demand media content, broadcast media content, and/or any othersimilar media content items. As used herein, the term “rating source”refers to any person, group of people, and/or entity that provides arating and/or comments regarding a program and/or other media contentasset. For example, a rating source may be a person who is aprofessional movie critic, a panel of experts, a husband and wife, afamily, a viewer, a customer, a subscriber, and/or any other person orentity who rates a program and/or provides comments regarding theprogram.

Although in many cases, expert ratings of programs can be of value, inthe context of rare and/or niche content, the ratings received fromviewers/customers are often more helpful than ratings received fromexperts. In many cases rare and/or niche content will not have beenrated by experts, and even when such programs have been rated byexperts, in many cases the expert will not share the interests of theniche viewer, and accordingly the review received from the expert maynot be very useful to the niche viewer.

FIG. 1 illustrates the viewer/rating source 104 and the rating source106 as two individual blocks. This is to emphasize that in someimplementation a rating source can provide ratings without being aviewer, as indicated by reference number 106, and that alternatively therating source can be both a viewer and rating source as indicated byreference number 104. In other words, the viewer/rating source 104 mayprovide a rating for a program and/or other media content item which theviewer/rating source 104 accesses, while the rating source 106 mayprovide ratings for a program and/or other media content item which therating source 106 has not accessed. Accordingly, the “viewer/ratingsource” 104 is also referred to simply as a “viewer” herein. Further,when describing the viewer 104 who may be shown a rating and/or commentsregarding a program, the viewer 104 is referred to as a “potentialviewer” herein. Similarly, when discussing the viewer 104 who may beinterested in rare or niche content, the viewer 104 is referred to as a“niche viewer” herein.

The program rating system 102 includes a rating collection/distributionservice 108, a rating adjusting service 110, a rating database 112, andcan also include one or more associated Websites 114. The viewer 104 canuse a television-based client device 116 and/or a computing-based device118 to submit a rating for a program and/or comments regarding theprogram to the program rating system 102 as described herein. Similarly,the rating source 106 can use a television-based client device 120and/or a computing-based device 122 to submit a rating for a programand/or comments regarding the program to the program rating system 102as described herein.

FIG. 2 illustrates an exemplary environment 200 in which further aspectsof embodiments of automatic rating optimization can be described. Theexemplary environment 200 includes the television-based client device116, a display device 202, content provider(s) 204, and an input device206 such as a remote control device. The exemplary environment 200 mayalso include the computing device 118, and an input device 208 such as acomputer keyboard and/or mouse. The input devices 206 and 208 can beused by the viewer/rating source 104 to interact with the televisionbased client device 116 and/or to interact with the computing device118. For example, the viewer 104 can use one or more of the inputdevices 206 and 208 to input ratings of programs, input commentsregarding programs, and/or for other purposes such as navigatinginteractive on-screen menus and/or program guides, and/or for selectingprograms and/or other media content assets for viewing. The viewer 104may also use the input devices 206 and 208 for navigating to the Website114 which is associated with the program rating system 102 as describedherein.

The display 202 can by any type of television, liquid crystal display(LCD), or similarly display system that renders audio, video, and/orimage data. The computing device 118 can be any type of computing devicewhich is capable of communicating ratings and/or comments to the contentprovider 204. Although illustrated as a desktop computer, the computingdevice 118 can alternatively be a portable device such as a cell phone,a combination device such as a personal digital assistant (PDA), alaptop computer, and/or any other similar computing device.

The client device 116 can be implemented in any number of embodiments,such as a set-top box, a digital video recorder (DVR) and playbacksystem, and/or as any other type of client device that may beimplemented in a television-based entertainment and information system.In the illustrated example, the client device 116 includes one or moreprocessor(s) 201, recording media 212 for maintaining recorded mediacontent 214, a playback application 216, and a rating application 218which can be implemented as computer executable instructions andexecuted by the processor(s) 210 to implement embodiments of automaticrating optimization. The client device 116 can also include mediacontent 220 which can be any form of on-demand and/or broadcast mediacontent. Additionally, the television-based client device 116 can beimplemented with any one or combination of the components described withreference to a client device 600 shown in FIG. 6. Further, the clientdevice 116 and display device 202 together are but one example of atelevision-based system, examples of which are described with referenceto the exemplary entertainment and information system 700 shown in FIG.7.

The recording media 212 along with the playback application 216 can beimplemented as a digital video recording (DVR) system to record andmaintain the recorded media content 214. The recorded media content 214may be any form of broadcast and/or on-demand media content 220 that theclient device 116 receives and records. Further, in someimplementations, the client device 116 may access or receive additionalrecorded media content from one or more remote data stores (not shown).According to exemplary embodiments, the playback application 216 is avideo control application which can be implemented to control theplayback of the media content 220 and/or to control the playback of therecorded media content 216 for viewing on the display device 202.

In some instances, DVR systems or video on demand (VOD) services thatimplement the description herein may provide rating information on lessthan an entire instance of given media content. For example, suchsystems and/or servers may rate one or more constituent portions orsubsets of the media content by monitoring how many times those portionsare replayed by viewers and/or reviewers. The more often that a givenportion is “rewound” and repeated, the more highly that portion may berated. In addition, where the content is structured into chapters, orother suitable organizational construct, the content appearing in thechapters may be rated based on reviews of the chapters.

In the illustrated example, the television-based client device 116 isconfigured for communication with the content provider(s) 204 via acommunication network 222, which in this example is an Internetprotocol-based (IP-based) network. The client device 116 can receiveprograms, media content, and/or program guide data, from the contentprovider(s) 204 via the IP-based network 222.

The content provider 204 includes a data store 224 which stores variousmedia content assets and/or programs 226 which can be communicated tothe client device 116, and also includes the program rating system 102.The program rating system 102 includes the ratingcollection/distribution service 108, the rating adjustment service 110,the rating database 112, and can also include the associated Website orWebsite hosting application 114.

Although the rating adjusting service 110 and the ratingcollection/distribution service 108 are each illustrated and describedas single application programs, the rating adjusting service 110 and therating collection/distribution service 108 can be implemented as severalcomponent applications distributed to each perform one or more functionsin the program rating system 102. Further, although the rating adjustingservice 110 and the rating collection/distribution service 108 areillustrated and described as separate application programs, the ratingadjusting service 110 and the rating collection/distribution service 108can be implemented together as a single application program in thecontent provider 204 to implement embodiments of automatic ratingoptimization.

Similarly, although the rating database 112 and the website 114 are eachillustrated and described as being located at the content provider 204,the rating database 112 and the website 114 can be implemented asseveral component applications and/or data stores distributed to eachperform one or more functions in the program rating system 102. In oneimplementation, a Web browser can be implemented in the television-baseddevice 116 and used to access the Website 114 via a communicationnetwork 228, which may be for example the Internet. The Web browser canalso be implemented in the television-based device 116 and used toaccess the Website 114 via the communication network 222.

In the illustrated example, the computing device 118 is also configuredfor communication with the Website 114 which is hosted by the contentprovider 204 via the communication network 228 (e.g., the Internet).Therefore, the viewer-rating source 104 may use the television-basedclient device 116 and/or may use the computing device 118 to access theWebsite 114 to communication program ratings, comments regardingprograms, and/or other information to the content provider 204 via theWebsite 114. The program ratings, comments regarding programs, and/orother information which have been communicated to the content provider204, are then communicated to the rating collection/distribution service108, and to the rating adjusting service 110 to implement variousaspects of automatic rating optimization. The program ratings, commentsregarding programs, and/or other information which have beencommunicated to the content provider 204 can be stored in the ratingdatabase 112.

With this background, and with general reference to FIGS. 1 and 2, onecan appreciate various implementations of automatic rating optimization.For example, the viewer/rating source 104 may provide a rating for aprogram and/or comments regarding the program to the program ratingsystem 102. The viewer 104 may submit the program rating and/or commentsregarding the program to the program rating system 102 using thetelevision-based client device 116 and/or using the computing device118.

In one implementation the television-based client device 116 generates agraphical user interface (not shown) which is displayed for the viewer104 on the display device 202. The viewer/rating source 104 uses one ormore of the user interface devices 206 and 208 to navigate the graphicaluser interface and to enter a rating and/or to provide commentsregarding and/or critiquing the program. Such ratings and/or commentsfor a program can be received from one or more rating sources, such asthe viewer/rating source 104, and these ratings can be stored in therating database 112.

In another implementation, the viewer/rating source 104 can use a Webbrowser implanted in the computing-based device 118 and/or implementedin the television-based client device 116 to access the Website 114 viathe communication network 228. Once the viewer 104 has accessed theWebsite 114, the viewer can navigate the Website 114 and enter a ratingfor the program and/or provide comments regarding the program using oneor more of the user interface devices 206 and 208. Such ratings and/orcomments for a program can be received from one or more rating sources,such as the viewer/rating source 104, and these ratings can be stored inthe rating database 112.

According to exemplary embodiments, the ratings for a program and/orcomments regarding the program are then communicated to the potentialviewers 104 of the program. The rating adjusting service 110 of theprogram rating system 102 counts access events for the program over aduration of time and determines how the ratings provided by each of therating sources 104 affect popularity of the program. Standard datamining techniques can be used to determine whether a rating given orassigned by a particular rating source, such as the rating source 104,changes the behavior of the potential viewers 104. For example, one ormore computers can be configured to determine how access eventscorrelated to ratings received from the particular rating source 104. Asused herein, the term “access events” refers to the viewer 104 selectinga program or other media content asset. For example, this can simply beselecting the program via an electronic programming guide and/or can beselecting and/or purchasing a program (e.g., a video on demand (VOD)program), and/or any other similar events. Such access events can beeasily tracked, counted, and measured.

By way of example, if the rating source 104/106 rates a particularprogram very highly (e.g., with five stars and/or with a laudatorydescription), access events following communication of the rating to thepotential viewers 104 can be easily tracked and counted, such that onecan determine how the rating affects access events for and/or sales ofthe program during a period of time (e.g., over the next four weeks).Over time, additional rating sources 104/106 will also rate the program,and these additional ratings can also be communicated to the potentialviewers 104. Once again, access events (or sales) followingcommunication of the additional rating can be tracked and counted, suchthat one can determine how the ratings received from each of theadditional rating sources 104/106 affect sales of the program during asubsequent period of time (e.g., over the four week period). Ratingsfrom some of the additional rating sources 104/106 may have a positiveeffect on sales of the program, while additional rating from othersources 104/106 may have a negative affect or no effect on sales of theprogram. The measured affect provides an approximation of whether therating source 104/106 provided ratings that were pertinent to thepotential viewers. In other words, ratings received from a particularrating source, such as the rating source 104/106, may have a highcorrelation with a potential viewer's preferences, a low correlationwith a potential viewer's preferences, or even an inverse correlationwith a potential viewer's preferences.

An overall rating (e.g., stars rating, points, letter grade, etc.) isthen determined for the program. More specifically, the overall ratingfor a given program may be multi-dimensional, in the sense that theoverall rating may be based on a series of ratings that considerdifferent criteria. For example, a given movie might receive a firstrating as an action movie, a second rating as a science fiction move, athird rating as drama, a fourth rating that reflects its appeal to horseenthusiasts, and so on. Once all these dimensions are compared betweenthe program and a viewer, an aggregate rating can be assigned across allthe dimensions.

When determining the overall rating for the program, the weight accordedto ratings received from the different rating sources can be adjusted sothat rating sources which are more accurate (e.g., those which have apositive correlation to potential viewers preferences and/or increasesales) are given more weight, and so that rating sources which are lessaccurate (e.g., those which have a negative correlation to potentialviewers preferences and/or decrease sales) are given less weight.

The overall rating for the program is then sent to the potential viewers104 who may or may not be interested in viewing the program. The ratingadjusting service 100 once again determines how the ratings and/orcomments provided by the rating sources 104/106 affects popularity ofthe program and the overall rating can be continuously and/orperiodically modified based as the weight accorded to the differentrating sources 104/106 is adjusted.

In one implementation the rating sources 104/106 (i.e., thoseindividuals, groups, or entities who provide ratings) are provided withindications of the weights which have been accorded to their variousratings. These indications can be provided via the rating sources104/106 via any suitable means, for example, the indications can beprovided using emails, using pop-ups and/or using Website summaries.

Similarly, the potential viewers 104 who are presented with the ratingsand/or comments from the ratings sources 104/106 can be provided withratings optimized indications, or otherwise be informed that the ratingshave been “optimized”. These indications can be provided to thepotential viewers 104 via any suitable means, for example, theindications can be provided using emails, using pop-ups, and/or Websitesummaries. In addition, links to more detailed information andstatistics regarding the “optimized” ratings can be provided to thepotential viewers 104. By way of example, this more detailed informationcan include details regarding the number of rating sources 104/106 thatwere included in generating the overall program rating, and/or detailsregarding the degree to which original ratings were modified by changingweights (from default and/or neutral weights), and/or may provideadditional data regarding the rating sources 104/106, and/or any otherinformation which may help potential viewers 104 analyze or betterunderstand the optimized ratings.

In some implementations of automatic rating optimization, the contentprovider 204 can monitor the rating sources 104/106 to determine variouscharacteristics about the ratings sources' 104/106 viewing and/ornon-viewing of a program which was rated, and can then adjust the weightaccorded to each of the rating sources 104/106 accordingly. For example,the content provider 204 can ascertain whether the rating source 104/106accessed the program which it rated, and then adjust the weight accordedto ratings received from the rating source 104/106 based on theascertaining. The rating adjusting service 100 may accord less weight toa rating received from a rating source, such as the rating source 106,which has not accessed the program which was rated, and more weight to arating received from a rating source, such as the rating source 104,which has accessed the program.

As another example, the content provider 204 can monitor how much of aprogram which was rated by the rating source 104 was displayed at therating source 104, and can adjust the weight accorded to ratingsreceived from the rating source based on the monitoring. The ratingadjusting service 110 may accord less weight to a rating received fromthe rating source 104 which displayed only a short segment of theprogram which was rated, and may accord more weight to a rating receivedfrom the rating sources 104 which displayed the complete program whichwas rated.

As yet another example, the content provider 204 can monitor whether theprogram rated by the rating source 104 was accessed on multipleoccasions by the rating source 104, and can adjust the weight accordedto ratings received from the rating source 104 based on the monitoring.The rating adjusting service 110 may accord less weight to a ratingreceived from the rating source 104 which displayed the program on onlyone occasion, and may accord more weight to a rating received from therating source 104 which displayed the complete program on multipleoccasions.

FIG. 3 illustrates exemplary graphs 300 that describe further aspects ofembodiments of automatic rating optimization. Graphs 302 and 304illustrate how popularity of a given instance of content (e.g., “ContentA”) relates to a position of the program on a long-tail curve or graph.As used herein, the term “long-tail” refers to a feature of statisticaldistributions where a high frequency or high-amplitude population isfollowed by a low-frequency or low-amplitude population which graduallydiminishes or “tails off”. In the context of programs and/or mediacontent items, such a distribution will typically show a long tail ofrare or niche content that does not enjoy a large amount of popularity,yet still nevertheless attracts the niche viewers 104. Accordingly, theterm “long-tail content” is used here to refer to rare or niche contentthat does not initially enjoy a large amount of popularity. Advances intechnology have allowed the content providers 204 to make increasingamounts of niche content or “long-tail content” available to thepotential viewers 104. Automatic rating optimization benefits theviewers 104 by making niche content more easily accessible, and alsobenefits the content providers 204 by allowing the content providers 204to increase profits through the provision of the niche content and/orthrough increased advertising revenues associated with provision of suchcontent.

Turning to the graphs 302 and 304 in more detail, the vertical axes ofthese graphs represent how frequently various instances of content havebeen consumed, varying from low to high. The horizontal axes of thesegraphs represent the popularity of content items, arranged from high tolow. Curves 306A and 306N (collectively, curves 306) indicate how manyinstances of content have achieved given levels of popularity.

Various implementations of automatic rating optimization provideadditional ways for determining how the ratings provided by each of therating sources 106/104 affect popularity of a program. Morespecifically, the techniques for providing automatic rating optimizationmay determine a correlation between a instance in which a reviewer ratesa given program, and any changes in the popularity of that program thatresult from that rating. In this manner, these techniques may identify“hit spotters”, or, put differently, those reviewers whose ratings aregiven credibility by increased levels of measured popularity.Additionally the “hit spotter's” rating may serve as a guide forrepresenting the likely perceptions of a number of other reviewers.

For example, the determination can include evaluating a rate of changein popularity of the program over one or more time periods (one or moredurations of time). More specifically, the graph 302 may provide a firstsnapshot of popularity levels taken at an arbitrary first time, denoted“Time 1”. The graph 304 may provide a second snapshot of the popularitylevels taken at an arbitrary second time, denoted “Time 2”, that isshown along with the snapshot taken at Time 1. As another example, thedetermination can include evaluating a rate of change in popularity ofthe program over the duration of time and also evaluating anacceleration/deceleration of the change in popularity of the programover the duration of time.

The speed or rate of popularity change (i.e., “S”) for a program can becalculated as follows:S=(Pop 2−Pop 1)÷(Time 2−Time 1)where “S” is the speed of popularity change at the end of a particulartime period, where “Pop 2” is the position of popularity of the programat “Time 2” (i.e., the end of the time period), and where “Pop 1” is theposition or popularity of the program at “Time 1” (i.e., the beginningof the time period). Such a time period or duration of time is indicatedby reference number 308.

The acceleration of popularity change (i.e., “A”) for a program can becalculated as follows:A=(S2−S1)÷(Time 2−Time 1)where “A” is the acceleration of popularity change at the end of aparticular time period, where “S2” is the speed of popularity change at“Time 2” (the end of the time period), and where “S1” is the speed ofpopularity change at “Time 1” (the beginning of the time period). Ofcourse, one may calculate the derivative of the velocity which is theacceleration.

Implementations of automatic rating optimization use the speed ofpopularity change (i.e., “S”) and/or the acceleration of popularitychange (i.e., “A”) for a program (e.g., Program A) to more accuratelyanalyze and determine the affect that a rating from a particular ratingsource has had on the popularity of a program. This can be accomplishedby periodically measuring program popularity, by periodicallycalculating the speed of popularity change and/or acceleration of thepopularity change for the program, and by periodically adjusting weightaccorded to ratings received from the rating source 104/106 according tothe results of these measurements and calculations. In this way, theoverall ratings of program which are provided to the potential viewers104 can be continually optimized, so that the overall ratings willemphasize the rating sources 104/106 which have been most relevant andimpact-full over time.

Implementation of automatic rating optimization can also be configuredto operate in different modes based on: (a) time windows (one or moredurations of time); and/or (b) rating volume threshold; and/or, (c)speed and/or acceleration of a program on the long-tail curve; and/or(d) extent of movement of a program on the long-tail curve. Further,when a program initially becomes available, a grace period can beallowed for sufficient rating and/or comments to be collected and forsufficient changes in popularity of the program to occur. Increasinglyrigorous weightings can be implemented once the program has beenavailable for a sufficient duration of time, and/or when rating volumeincreases above a threshold, and/or when the content item's popularitychanges dramatically, and/or after other indicators show that morerigorous weighting would be helpful. In other words, adjustment of theratings for the various sources 104/106 can be initiated when suchadjustments are warranted based on any suitable criteria.

Embodiments of automatic rating optimization also prove that the viewer104 can be associated with a preference profile based on viewing habitsof the viewer 104, and that the rating sources 106/104 can be associatedwith preference profiles based on viewing habits of the rating sources106/104, so that each rating source 106/104 is associated with one ofthe preference profiles. A degree of relatedness between the preferencesprofile of the viewer 104 and the preference profiles of each of therating sources 106/104 can be determined, and a weight accorded toratings received from each of the rating sources 106/104 can be adjustedbased on the determining degree of relatedness.

Automatic rating optimization provides that preference profiles can beestablished for the viewers 104 and/or the rating sources 106/104, andthat such preference profiles can be used to generate more accuraterating of programs. In addition, the viewing habits of the viewer 104can be monitored, so that a weight accorded to program ratings and/orrecommendations sent to the viewer 104 can be adjusted according to theviewer's preferences.

In one implementation the viewer 104 is presented with a customizedrating base on their personal profile. The program rating presented toone potential viewer 104 can be different than the rating presented toanother potential viewer 104 for the same program, because the potentialviewers' individual preference profiles can be taken into considerationand the rating can be adjusted accordingly. For example, a lower weightcan be accorded to ratings received from the rating sources 106/104 thatare in profiles distant from the viewer's personal profile, while moreweight can be accorded to the ratings received from the rating sources106/104 that are in profiles proximate the viewer's profile.

In another implementation the viewers 104 are associated with a group ofviewers 104 based on shared viewing preferences, and each viewer 104 inthe group is presented with program ratings which have been tailoredand/or adjusted based on a group profile. In such implementation, aprogram rating presented to one group of potential viewers 104 can bedifferent than the rating presented to another group of potentialviewers 104 for the same program, because the potential viewers' groupprofiles can be taken into consideration, and the rating can beadjusting accordingly.

The profiles can be used by the program rating system 102 to identifythe niche viewers 104 that may have similar viewing interests and/orprogramming preferences. The program rating system 102 can use theprofiles of the viewers 104, and/or the rating sources 106/104 in aflexible way to match interests and provide the viewers 104 with ratingand/or recommendations which will be more trustworthy and applicable tothe viewer's interests. For example, a group of viewers 104 who areinterested in a particular form of dance may receive ratings which arebased on reviews and/or ratings by others who share this commoninterest, and thus able to provide reviews which are applicable to theunique interests of the group of viewers 104.

Preferences of one or more of the rating sources 104/106 can be used togenerate a moderator channel. Alternatively, the preferences used toset-up the moderator channel can be provided by the content provider 204and/or can be provided by some other source. The moderator channelprovides program ratings and/or recommendations which are weighted basedon a particular set of preferences which define the moderator channel.For example, the potential viewer 104 may select to have ratings and/orrecommendations weighted according to a particular moderator channel inwhich ratings are adjusted for the viewers 104 who like science fictionbut dislike mysteries. Any number of differently weighted moderatorchannels can be established to aid the viewers 104 with differentviewing preferences, by providing useful ratings which will help themlocate programs they will enjoy watching.

In light of the description herein, one can appreciate that automaticrating optimization provides systems and methods which assist theviewers 104 in finding content which they will want to view by providingcontent ratings the viewers 104 can trust. By using feedback fromcontent access counting as described herein, the weight accorded toprogram ratings received from various rating sources 104/106 can bedifferently adjusted. The weight accorded to the ratings received fromthe various rating sources 104/106 can be adjusted based on the degreeto which past ratings from those particular rating sources 104/106 havetranslated into content position and change in position on the long-tailcurve (measured via content access counting).

Implementations of automatic rating optimization provide that eachcontent item/program can be separately measured and/or that each ratingsource 104/106 can be separately weighted. An aggregation of ratings fora particular content item/program from weighted rating sources can becalculated, and the aggregation provides an overall rating for thatcontent item/program. The overall rating can be continuously optimizedbased on additional feedback from the content access counting.Additionally, if desired, profiles can be developed for the ratingsources 104/106 and/or for the viewers 104, and can be used to providecustomized accuracy. Implementations of automatic rating optimizationtie ratings to results (e.g., content access counts) for individualcontent items/programs, to groups of the rating sources 104/106, and togroups of the viewers 104. This allows viewers 104 to know that theratings can be trusted, are useful, and that the ratings can be reliablyused to select content/programs.

Methods for automatic rating optimization, such as exemplary methods 400and 500 described with reference to respective FIGS. 4 and 5, may bedescribed in the general context of computer executable instructions,Generally, computer executable instructions can include routines,programs, objects, components, data structures, procedures, modules,functions, and the like that perform particular functions or implementparticular abstract data types. The methods may also be practiced in adistributed computing environment where functions are performed byremote processing devices that are linked through a communicationsnetwork. In a distributed computing environment, computer executableinstructions may be located in both local and remote computer storagemedia, including memory storage devices.

FIG. 4 illustrates the exemplary method 400 for automatic ratingoptimization and is described with reference to the exemplaryenvironment 100 shown in FIG. 1, and with reference to the exemplaryenvironment 200 shown in FIG. 2. The order in which the method isdescribed is not intended to be construed as a limitation, and anynumber of the described method block can be combined in any order toimplement the method, or an alternate method. Furthermore, the methodcan be implemented in any suitable hardware, software, firmware, orcombination thereof.

Block 402 represents receiving ratings of a program from one or morerating sources. For example, the program rating system 102 can receiveone or more ratings for a program from the viewers/rating sources 104and/or from the rating sources 106. The ratings can be entered by theviewer/rating sources 104 via the television-based device 116 and/or viathe computing device 118, and/or can be entered by the rating sources106 via the television-based client device 120 and/or via the computingdevice 122.

Block 404 represents sending a representation of a program selectionmechanism to potential viewers of the program. More specifically, block404 may include using the rating information in various algorithms inorder to help the potential viewers 104 select a desirable program. Inother words, the ratings may enable the program selection mechanism tocompile a channel listing or play list, a menu of suggested programs, orthe like.

In some instances, presenting the representation to the viewers 104 mayinclude directly presenting one or more ratings for a given instance ofcontent, such that the potential viewer 104 may directly examine,browse, compare, and/or further access the one or more ratingsdetermined for each content item.

Additionally, the representation presented to consumers may takedifferent forms, in different possible implementations. For example, insome implementations, the representation may include a visualrepresentation, whether in image, video, or other form. In otherimplementations, the representation may include audible or audioaspects, for example, when the consumer is visually impaired. In stillother implementations, the representation may combine visual and audibleaspects into a multimedia presentation. The representation of theratings may also be customized, in the sense that the ratings may bereflected in the order in which the items are presented in a list, theassignment of a channel as presented to a viewer, or the presence in alisting after the techniques described herein have culled out lessrelevant content.

Block 406 represents determining how the ratings provided by each of therating sources 104/106 affect popularity of the program by countingaccess events for the program over a duration of time. For example, oneor more computers can be configured to determine how access eventscorrelated to ratings received from the particular rating source104/106, and standard data mining techniques can be used to determinewhether a rating given or assigned by the particular rating source104/106 changed the behavior of the potential viewers 104.

Block 408 represents adjusting one or more weights accorded to pastratings received from the rating sources on the current ratings. Forexample, if the rating source's 104/106 prior ratings have had apositive affect on popularity of programs which it rated, a higherweight can be accorded to ratings received from that rating source104/106. On the other hand, if the rating source's 104/106 prior ratingshave had a negative affect on popularity of programs which it rated, alower weight can be accorded to ratings received from that rating source104/106. By adjusting the weight accorded to the ratings received fromdifferent rating sources 104/106, a more useful overall rating can beassigned to a program.

Block 410 represents ascertaining whether the rating source 104/106accessed the program which it rated. For example, the content provider204 can ascertain whether the rating source 104/106 accessed the programwhich it rated.

Block 412 represents adjusting the weight accorded to ratings receivedfrom the rating source 104/106 based on the ascertaining. For example,the rating adjusting service 110 may accord less weight to a ratingreceived from the rating source 104/106 which has not accessed theprogram which was rated, and more weight to a rating received from therating source 104/106 which has accessed the program.

Block 414 represents monitoring how much of the program was displayed atthe rating source 104 which rated the program. For example, the contentprovider 204 can monitor how much of a program which was rated by therating source 104 was displayed at the rating source 104.

Block 416 represents adjusting the weight accorded to ratings receivedfrom the rating source 104 based on the monitoring. For example, therating adjusting service 110 may accord less weight to a rating receivedfrom the rating sources 104 which displayed only a short segment of theprogram which was rated, and may accord more weight to a rating receivedfrom the rating sources 104 which displayed the complete program whichwas rated.

Block 418 represents detecting whether the program was accessed onmultiple occasions by the rating source 104 which rated the program. Forexample, the content provider 204 can monitor whether the program ratedby the rating source 104 was accessed on multiple occasions by therating source 104.

Block 420 represents adjusting the weight accorded to ratings receivedfrom the rating source based on the detecting. For example, the ratingadjusting service 110 may accord less weight to a rating received fromthe rating sources 104 which displayed the program on only one occasion,and may accord more weight to a rating received from the rating sources104 which displayed the complete program on multiple occasions.

FIG. 5 illustrates the exemplary method 500 for automatic ratingoptimization and is described with reference to the exemplaryenvironment 100 shown in FIG. 1, and with reference to the exemplaryenvironment 200 shown in FIG. 2. The order in which the method isdescribed is not intended to be construed as a limitation, and anynumber of the described method blocks can be combined in any order toimplement the method, or an alternate method. Furthermore, the methodcan be implemented in any suitable hardware, software, firmware, orcombination thereof.

Block 502 represents associating a viewer with a preference profilebased on viewing habits of the viewer. For example, the viewer 104 canbe associated with a preference profile based on the viewing habits ofthe viewer 104. This can involve monitoring the viewer's 104 viewinghabits.

Block 504 represents associating the rating sources 104/106 withpreference profiles based on viewing habits of the rating sources104/106, such that each rating source 104/106 is associated with one ofthe preference profiles. For example, other viewer/rating sources 104can be associated with a preference profile based on the viewing habitsof the other viewer/rating sources 104. This can involve monitoring theother viewer/rating sources' 104 viewing habits.

Block 506 represents determining a degree of relatedness between thepreference profile of the viewer 104 and the preference profiles of eachof the rating sources 104/106. For example, the profile of the oneviewer 104 can be compared to the profile of the other viewer/ratingsource 104 to determine a degree of relatedness between the preferenceprofile of the one viewer 104 and the preference profiles of each of theother viewer/rating sources 104.

Block 508 represents adjusting a weight accorded to rating received fromeach of the rating sources 104/106 based on the determining of thedegree of relatedness. For example, the weight accorded to ratingsreceived from each of the other viewer/rating sources 104/106 can beadjusted based on the determining of the degree of relatedness to theone viewer 104.

FIG. 6 illustrates various components of the exemplary client device 600which can be implemented as any form of a computing, electronic, ortelevision-based client device in which embodiments of automatic ratingoptimization can be implemented.

The client device 600 includes one or more media content inputs 602which may include Internet Protocol (IP) inputs over which streams ofmedia content can be received via an IP-based network (such as thenetwork 222 of FIG. 2). The client device 600 also includes one or morecommunication interface(s) 604 which can be implemented as any one ormore of a serial and/or parallel interface, a wireless interface, amodem, a network interface, and as any other type of suitablecommunication interface. The communication interface 604, such as awireless interface, enables client device 600 to receive control inputcommands 606 and/or other information from an input device, such as fromremote control device 608, mobile computing device, mobile telephone,and/or similar input device. The communication interface 604, such as anetwork interface, can be implemented to provide a connection betweenthe client device 600 and a communication network by which otherelectronic and computing devices can communicate data with the device600. The communication interface 604, such as a serial and/or parallelinterface, can be implemented to provide for data communication directlybetween the client device 600 and the other electronic or computingdevices. The communication interface 604, such as a modem, can beimplemented to facilitate communication with other electronic and/orcomputing devices via a conventional telephone line, a digitalsubscriber line (DSL) connection, cable, and/or via any other type ofsuitable connection.

The client device 600 also includes one or more processors 610 (e.g.,any of microprocessors, controllers, and/or similar devices) whichprocess various computer executable instructions to control operation ofthe device 600, and to communicate with other electronic and computingdevices, and to implement various embodiments of automatic ratingoptimization. The client device 600 can be implemented with computerreadable media 612, such as one or more memory components. Examples ofsuch memory components include random access memory (RAM), non-volatilememory, tape storage, and/or a disk storage device. A disk storagedevice can include any type of magnetic or optical storage device, suchas a hard disk drive, a compact disc (CD), a digital video disc (DVD),and/or any other similar device.

The computer readable media 612 provides data storage mechanisms tostore various information and/or data such as software applicationsand/or any other types of information and data related to operation ofthe client device 600. For example, an operating system 614 and/or otherapplication program 616 can be maintained as software applications withthe computer readable media 612 and executed on the processor(s) toimplement embodiments of automatic rating optimization.

For example, the client device 600 can be implemented to include aprogram guide application 618 that is implemented to process programguide data 620 and generate program guides for display which enable aviewer to navigate through an onscreen display to locate broadcastprograms, recorded programs, video on-demand programs/movies, and/orother media content assets of interest to the viewer.

The client 600 can also include a DVR system 622 with a playbackapplication 624, and recording media 626 to maintain recorded mediacontent 628 which may be any form of on-demand and/or media content suchas programs, movies, video, and/or image content that the client device600 receives and/or records. The playback application 624 is a videocontrol application that can be implemented to control the playback ofmedia content/programs, the recorded media content 628, and/or othervideo on-demand media content which can be rendered and/or displayed forviewing.

The client device 600 also includes an audio and/or video output 630that provides audio and/or video to an audio rendering and/or displaysystem 632. Video signals and audio signals can be communicated from thedevice 600 to the display device 632 via any suitable communicationlink. Alternatively, the audio rendering and/or display system 632 canbe integrated components of the exemplary client device 600.

The client device 600 also includes a rating application 634 thatreceives ratings form programs and/or comments regarding programs whichhave been received from one or more rating sources (e.g., 104 and 106).The rating application 634 implements embodiments of automatic ratingoptimization as described herein.

FIG. 7 illustrates an exemplary entertainment and information system 700in which embodiments of automatic rating optimization can beimplemented. The system 700 facilitates the distribution of mediacontent/programs and/or program guide data to multiple viewers and/or tomultiple viewing systems. The system 700 includes a content provider 702and television-based client systems 704(1-N) which are each configuredfor communication via an IP-based network 706. Each of thetelevision-based client systems 704(1-N) can receive one or more datastreams from the content provider 702, and the data streams can bedistributed to one or more other television-based client devices and/orcomputing systems.

The network 706 can be implemented as a wide area network (e.g., theInternet), an intranet, a Digital Subscriber Line (DSL) networkinfrastructure, and/or as a point-to-point coupling infrastructure.Additionally, the network 706 can be implemented using any type ofnetwork topology, any network communication protocol, and/or can beimplemented as a combination of two or more networks. A digital networkcan include various hardwired and/or wireless communication links708(1-N), routers, and so forth to facilitate communication between thecontent provider 702 and the client systems 704(1-N). Thetelevision-based client systems 704(1-N) receive media content, programcontent, program guide data, and/or similar content items from contentserver(s) of the content provider 702 via the IP-based network 706.

The content provider 702 is representative of a head-end service in atelevision-based content distribution system. Such a head-end serviceprovides for example, the media content and/or program guide data and/orsimilar content to multiple viewers/subscribers (e.g., thetelevision-based client systems 704(1-N)). The content provider 702 canbe implemented as a cable operator, a satellite operator, a networktelevision operator, and/or similar operator to control distribution ofmedia content, programs, movies, television programs, and/or othersimilar media content assets to the client systems 704(1-N).

The content provider 702 can include various components to facilitatemedia data processing and content distribution, and/or can be linked toother various remote components which facilitate such media dataprocessing and content distribution.

The television-based client systems 704(1-N) can be implemented toinclude the television-based client services 710(1-N) and a displaydevice 712(1-N) (e.g., a television, LCD, and/or similar device). Thetelevision-based client device 710 of the television-based client system704 can be implemented in any number of embodiments, such as a set-topbox, a digital video recorder (DVR) and playback system, an appliancedevice, a gaming system, and as any other type of client device that maybe implemented in a television-based entertainment and informationsystem. In one embodiment, one or more of the client systems 704 can beimplemented with a computing-based device. In the illustrated example,the client system 704(1-N) is implemented with a computing-based device714 as well as the television-based client device 704. Any of thetelevision-based client devices 710 and/or computing-based devices 714of the television-based client system 704 can implement features andembodiments automatic rating optimization as described herein.

FIG. 8 illustrates operating environments 800 that extend the previoustechniques for automatic rating optimization to portable wirelessdevices. For convenience, but not limitation, some features are carriedforward from the description above into FIG. 8, and denoted by the samereference numbers.

For conciseness of illustration only, FIG. 8 depicts several wirelesscommunications devices 802 a-n, associated with respective user 804 a-nas indicated by the dashed lines shown connecting these elements in FIG.8. The wireless communications devices 802 a-n may take any convenientform, and are not limited by the example devices shown in FIG. 8. Ingeneral, the operating environments 800 may include any number of users804 a-n or devices 802 a-n.

In the example illustrated in FIG. 8, the operating environments 800 mayenable the user 804 a to receive content from one or more contentproviders, which are carried forward into FIG. 8 as 702. FIG. 8 denotesthe provider content at 806. The operating environments 800 may enablethe user 804 a to receive and access the content 806 via a wirelessdevice, such as the mobile phone 802 a. The user 804 a may also rate theprovider content 806, with the rating denoted generally at 808. The user804 a may transmit this rating to a content rating system 102, which iscarried forward into FIG. 8 as 102.

As also shown in FIG. 8, the user 804 b may create user-origin content,denoted generally at 810. The operating environments 800 may enable theuser 804 b to send the content 810 to one or more other users via awireless device, such as the wireless PDA 802 b. More specifically, theuser 804 b may send the content 810 to the user 804 a and the user 804n. FIG. 8 denotes the content 810 as sent to the users 804 a and 804 nrespectively at 810 a and 810 n. In turn, the users 804 a and 804 n mayaccess the user-origin content 810 via wireless devices, such as therespective mobile phones 802 a and 802 n.

Having received and accessed the user-origin content 810, the users 804a and 804 n may rate the user-origin content 810, extending thetechniques described above. The users 804 a and 804 n may provideratings of the user-origin content 810 to the content rating system 201.For clarity of illustration, FIG. 8 depicts one instance of a rating ofthe user-origin content 810 at 812, as submitted by the user 804 n.However, one or more other users, such as the user 804 a, could alsosubmit ratings of the user-origin content 810 as well. In this manner,the content rating system 102 may provide a centralized store thatcontains ratings of not only the provider content 806, but also theuser-origin content 810.

Taken collectively, the users 804 a-n and/or the devices 802 a-n mayform a group, denoted generally at 814. In some instances, the group 814may be static in nature, and may include the users 804 a-n who arefriends, family, or have other relationships. In other instances, thegroup 814 may be dynamic or ad hoc in nature. For example, the group 814may include the users 804 a-n who may be in geographical or physicalproximity at a given time, or may share some common interest at thatgiven time.

The users 804 a-n who form the group 814 may submit ratings of contentto the group. FIG. 8 denotes these ratings generally at 816, withexamples of ratings originating from the users 804 a-n shownrespectively at 816 a-n. These ratings may include ratings of theprovider content 806 or the user-origin content 810. It is noted thatany of the user-origin content 810 rated at 816 may or may not includecontent submitted by users within the group 814. Instead, theuser-origin content 810 may originate from a user who is not within thegroup 814.

While FIG. 8 shows the group 814 for convenience, the operatingenvironments 800 may include any number of groups. Additionally, theusers 804 a-n may be members of one or more groups.

In the foregoing manner, the users 804 a-n within the group 814 maydefine “hits” or other forms of popular or highly-rated content withinthe context of the group. Additionally, the users 804 a-804 n who aremembers of more than one group may propagate highly-rated content fromone group to another, whether that content is the provider content 806or the user-origin content 810.

The ratings 816 a-n related to the groups 814 may be stored in datarecords maintained by, for example, the content rating system 102, asrepresented generally by the dashed line 818. However, in someinstances, these data records may also be maintained, at least in part,by another entity. In any implementation, the group ratings 816 a-n mayperiodically be uploaded or updated to the content rating system 102, asalso represented by the dashed line 818.

In some implementations of the description herein, the ratingscomponents may themselves be data elements that are related to thecontent. However, the ratings components may or may not be co-residentwith the associated content. More specifically, the ratings componentsmay be stored as sets of searchable, orderable descriptors that aresearchable or accessible separately from the associated content. Thus,the various operating environments described herein may enable users tosearch for ratings components meeting one or more selection criteria,and browse or parse the ratings components meeting those criteria.Afterwards, the users may then request to obtain the contentcorresponding to acceptable criteria. The users may submit this requestas a process that is separate from the ratings selection process.

In some instances, the ratings selection process and the process forobtaining the content may use different connections having differentbandwidths or capacities. As may be expected, transmitting or streamingthe content may impose heavier demands on the underlying communicationsconnection than would data transfers associated with the ratingscompounds.

Although embodiments of automatic rating optimization have beendescribed in language specific to features and/or methods, it is to beunderstood that the subject of the appended claims is not necessarilylimited to the specific features or methods described. Rather, thespecific features and methods are disclosed as exemplary implementationsof automatic rating optimization.

The invention claimed is:
 1. A method, comprising: receiving, by acontent rating system, ratings that are associated with a content,wherein the ratings are received from rating sources, wherein a firstone of the ratings is from a first one of the rating sources;ascertaining, by the content rating system, an indication of whether therating sources accessed the content; determining, by the content ratingsystem, an effect each of the ratings has on a popularity of the contentfor a time period after which the first one of the ratings is providedto at least one potential consumer of the content; ascertaining by thecontent rating system, preference profiles, wherein each of the ratingsources is associated with one of the preference profiles, wherein eachof the preference profiles is based on consumption habits of the ratingsources, wherein a first one of the preference profiles is associatedwith the first one of the rating sources and a second one of thepreference profiles is associated with a potential consumer of thecontent; determining, by the content rating system, a degree ofrelatedness between the first one of the preference profiles and thesecond one of the preference profiles; detecting a number of times thatthe content was accessed by the first one of the rating sources; andadjusting, by the content rating system, a weight of the first one ofthe ratings, wherein: the weight of the first one of the ratings isincreased if the first one of the ratings comprises a positive ratingand the popularity of the content increases in the time period; theweight of the first one of the ratings is increased if the first one ofthe ratings comprises a negative rating and the popularity of thecontent decreases in the time period; the weight of the first one of theratings is increased if the first one of the rating sources accessed thecontent; the weight of the first one of the ratings is decreased if thefirst one of the ratings comprises a negative rating and the popularityof the content does not decrease in the time period; the weight of thefirst one of the ratings is decreased if the first one of the ratingscomprises a positive rating and the popularity of the content does notincrease in the time period; the weight of the first one of the ratingsis decreased if the first one of the rating sources did not access thecontent; the weight of the first one of the ratings is increased if thedegree of relatedness comprises a high degree of relatedness; the weightof the first one of the ratings is decreased if the degree ofrelatedness comprises a low degree of relatedness; and the weight of thefirst one of the ratings is increased if the content was accessed onmultiple occasions by the first one or the rating sources.
 2. The methodof claim 1, further comprising: generating, by the content ratingsystem, an average rating for the content, the average rating comprisingat least two weighted ratings, wherein the first one of the ratings isone of the at least two weighted ratings; and providing, by the contentrating system, the average rating to a rating distribution systemconfigured to provide the average rating to potential consumers of thecontent.
 3. The method of claim 1, wherein determining, by the contentrating system, the popularity of the content comprises determining anumber of times the content is accessed.
 4. The method of claim 3,wherein determining, by the content rating system, the popularity of thecontent further comprises determining a first rate of change, whereinthe first rate of change is a rate of change of the number of times thecontent is accessed over a period of time.
 5. The method of claim 4,wherein determining, by the content rating system, the popularity of thecontent further comprises a second rate of change, wherein the secondrate of change is a rate of change of the first rate of change.
 6. Themethod of claim 1, further comprising ascertaining an indication of aportion of the content that was accessed by the first one of the ratingsources.
 7. The method of claim 6, wherein the adjusting furthercomprises: increasing the weight of the first one of the ratings if thefirst one of the rating sources accessed the content in its entirety;and decreasing the weight of the first one of the ratings if the firstone of the rating sources did not access the content in its entirety. 8.The method of claim 1, wherein the weight is an authority.
 9. A contentrating system, comprising: a processing system including a processor;and a memory that stores executable instructions that, when executed bythe processing system, facilitate performance of operations, comprising:receiving ratings that are associated with a content, wherein theratings are received from rating sources, wherein a first one of theratings is from a first one of the rating sources; ascertaining anindication of whether the rating sources accessed the content;determining an effect each of the ratings has on popularity of thecontent for a time period after which the first one of the ratings isprovided to at least one potential consumer of the content; ascertainingpreference profiles, wherein each of the rating sources is associatedwith one of the preference profiles, wherein each of the preferenceprofiles is based on consumption habits of the rating sources, wherein afirst one of the preference profiles is associated with the first one ofthe rating sources and a second one of the preference profiles isassociated with a potential consumer of the content; determining adegree of relatedness between the first one of the preference profilesand the second one of the preference profiles; detecting a number oftimes that the content was accessed by the first one of the ratingsources; and adjusting a weight of the first one of the ratings,wherein: the weight of the first one of the ratings is increased if thefirst one of the ratings if the first one of the ratings ratingcomprises a positive rating and the popularity of the content increasesin the time period; the weight of the first one of the ratings isincreased if the first one of the ratings comprises a negative ratingand the popularity of the content decreases in the time period; theweight of the first one of the ratings is increased if the first one ofthe rating sources accessed the content; the weight of the first one ofthe ratings is decreased if the first one of the ratings comprises anegative rating and the popularity of the content does not decrease inthe time period; the weight of the first one of the ratings is decreasedif the first one of the ratings comprises a positive rating and thepopularity of the content does not increase in the time period; theweight of the first one of the ratings is decreased if the first one ofthe rating sources did not access the content; the weight of the firstone of the ratings is increased if the degree of relatedness comprises ahigh degree of relatedness; the weight of the first one of the ratingsis decreased if the degree of relatedness comprises a low degree ofrelatedness; and the weight of the first one of the ratings is increasedif the content was accessed on multiple occasions by the first one ofthe rating sources.
 10. The content rating system of claim 9, furtherconfigured to generate an average rating for the content, the averagerating comprising at least two weighted ratings.
 11. The content ratingsystem of claim 10, further configured to send the average rating to acontent provider.
 12. The content rating system of claim 9, furtherconfigured to determine the popularity of the content by: counting anumber of times the content is accessed over a duration of time.
 13. Thecontent rating system of claim 12, further configured to determine thepopularity of the content by determining a rate of change of the numberof times the content is accessed over a period of time.
 14. The contentrating system of claim 13, further configured to determine thepopularity of the content by determining a rate of change of the rate ofchange of the number of times the content is accessed over a period oftime.
 15. The content rating system of claim 9, further configured todetermine a portion of the content that was accessed by the first one ofthe rating sources.
 16. The content rating system of claim 15, whereinadjusting the weight further comprises: increasing the weight of thefirst one of the ratings if the first one of the rating sources accessedthe content in its entirety; and decreasing the weight of the first oneof the ratings if the first one of the rating sources did not access thecontent in its entirety.
 17. The content rating system of claim 9,further configured to adjust a weight of the first one of the ratings,wherein adjusting the weight comprises: increasing the weight accordedto the first one of the ratings if the first one of the rating sourcesaccessed the content; and decreasing the weight accorded to the firstone of the ratings if the first one of the rating sources did not accessthe content.
 18. The content rating system of claim 9, furtherconfigured to: access preference profiles, wherein each of the ratingsources is associated with one of the preference profiles, wherein eachof the preference profiles is based on consumption habits of the ratingsources, wherein a first one of the preference profiles is associatedwith the first one of the ratings sources and a second one of thepreference profiles is associated with a potential consumer of thecontent; determine a degree of relatedness between the first one of thepreference profiles and the second one of the preference profiles; andadjust a weight of the first one of the ratings, wherein adjusting theweight comprises: increasing the weight accorded to the first one of theratings if the degree of relatedness comprises a high degree ofrelatedness; and decreasing the weight accorded to the first one of theratings if the degree of relatedness comprises a low degree ofrelatedness.
 19. A non-transitory machine-readable storage medium,comprising executable instructions that, when executed by a processingsystem including a processor, facilitate performance of operations,comprising: receiving ratings that are associated with a content,wherein the ratings are received from rating sources, wherein a firstone of the ratings is from a first one of the rating sources;ascertaining an indication of whether the rating sources accessed thecontent; determining an effect each of the ratings has on a popularityof the content for a time period after which the first one of theratings is provided to at least one potential consumer of the content;ascertaining preference profiles, wherein each of the rating sources isassociated with one of the preference profiles, wherein each of thepreference profiles is based on consumption habits of the ratingsources, wherein a first one of the preference profiles is associatedwith the first one of the rating sources and a second one of thepreference profiles is associated with a potential consumer of thecontent; determining a degree of relatedness between the first one ofthe preference profiles and the second one of the preference profiles;detecting a number of times that the content was accessed by the firstone of the rating sources; and adjusting a weight of the first one ofthe ratings, wherein: the weight of the first one of the ratings isincreased if the first one of the ratings comprises a positive ratingand the popularity of the content increases in the time period; theweight of the first one of the ratings is increased if the first one ofthe ratings comprises a negative rating and the popularity of thecontent decreases in the time period; the weight of the first one of theratings is increased if the first one of the rating sources accessed thecontent; the weight of the first one of the ratings is decreased if thefirst one of the ratings comprises a negative rating and the popularityof the content does not decrease in the time period; the weight of thefirst one of the ratings is decreased if the first one of the ratingscomprises a positive rating and the popularity of the content does notincrease in the time period; the weight of the first one of the ratingsis decreased if the first one of the rating sources did not access thecontent; the weight of the first one of the ratings is increased if thedegree of relatedness comprises a high degree of relatedness; the weightof the first one of the ratings is decreased if the degree ofrelatedness comprises a low degree of relatedness; and the weight of thefirst one of the ratings is increased if the content was accessed onmultiple occasions by the first one or the rating sources.
 20. Thenon-transitory machine-readable storage medium of claim 19, wherein theoperations further comprise: generating an average rating for thecontent, the average rating comprising at least two weighted ratings,wherein the first one of the ratings is one of the at least two weightedratings; and providing the average rating to a rating distributionsystem configured to provide the average rating to potential consumersof the content.